Flexible koala conservation on private land under climate change

Frankie Cho

University of Queensland

Flexible adaptation

  • Koalas declared endangered in NSW, QLD and ACT

  • Climate change threatens koala habitat

  • NSW Koala Strategy: 7,000ha koala habitat to be protected on NSW private land by 2025

7,000ha koala habitat on private land

Koala habitat: KITL index > 0.25

CC: Enough protection under current conditions

RI: Robust but inflexible protection

F: Flexible protection – add new covenants in 2050

F+L: Flexible protection + learning and adapting to climate change

Flexible adaptation 50% more cost-effective

CC: $51M ($44-61M)

RI: $108M ($101-117M)

F: $72M ($63-81M) – 37% cost reduction

Flexible + Learning: $59M ($48-74M) – 49% cost reduction

Flexibility: adding protection more valuable than ending

CC: $51M ($44-61M)

RI: $108M ($101-117M)

F: $72M ($63-81M) – 37% cost reduction

Key findings

  • Strategic flexibility alters first-stage decisions

  • Flexibility mitigates trade-offs between maximizing conservation outcomes and managing risks

  • Flexibility to offer new covenants much more valuable than flexibility to end interventions

Appendix 1: Sensitivity analysis

Sensitivities of estimates of the value of flexibility (A, E and A/E) under changes to the default parameters, showing sensitivities to a, sampling of 10 study populations randomly sampled with the stratified sampling approach (default index = 1), b, year covenant modification is allowed - t’ (default = 2050). c, koala landscape capacity indicator cut-off (default = 0.25) across a range of cut-offs where feasible solutions to the problem are found, and d. Amount of learning based on the number of climate scenarios decision-makers are uncertain about (1 being perfect certainty over climate change, default = 12), with “No Learning”, “Partial Learning” and “Full Learning” having parameters of 12, 3 and 1 respectively.

Appendix 2: Two-stage stochastic optimization

\[ \min_x E_{j \in J}\Big[\sum_i \sum_t c_{ijt}x_i + E_{S\in\xi}[Q(x,S)]\Big] \\ Q_j(x,S) := \min_{y,w} E_{s\in S}\Big[\sum_i \sum_{t\geq t'} c_{ijt}y_{is} - c_{ijt}w_{is}\Big] \\ s.t. \sum_i m_{ijts} x_i \geq K \quad \forall t=1,s\in S, j \in J \\ \sum_i m_{ijts}(x_i + y_{is} - w_{is} \geq K \quad \forall t \geq t', s \in S, j \in J \\ \sum_i m_{ijts} x_i \geq K \quad \forall t = 1, s \in S, j \in J \\ x_i + y_{is} \leq 1 \quad \forall i \in N, s \in S \\ x_i \geq w_{is} \quad \forall i \in N, s \in S \\ y_{is} = 0 \quad \forall i \in N, s \in S \\ w_{is} = 0 \quad \forall i \in N, s \in S \\ x,y,w \in [0,1] \]

Appendix 3

Conservation under Current Conditions (CC)

  • Total habitat reaches 7,000 ha in 2020 on average

Robust and Inflexible (RI)

  • Total habitat is higher than 7,000 for all time-steps, all climate scenarios and all social behavior realizations
  • Covenant offers same across time

Flexible (F)

  • Total habitat is higher than 7,000 for all time-steps, all climate scenarios and all social behavior realizations
  • New covenant offers can be added in 2050

Flexible with Learning (F+L)

  • Total habitat is higher than 7,000 for time-steps before 2050, all climate scenarios and all social behavior realizations
  • After 2050, total habitat must be higher than 7,000 for a particular climate scenario (offers can be different across scenarios) on the average social behavior realization
  • New covenant offers can be added in 2050